class_docstr: Factorized conv: ReLU - Conv(Kx1) - Conv(1xK) - BN
code: def __init__(
      self, C_in, C_out, kernel_length, stride, padding, affine=True
  ):
      super().__init__()
      self.net = nn.Sequential(
          nn.ReLU(),
          nn.Conv2d(
              C_in,
              C_in,
              (kernel_length, 1),
              stride,
              padding,
              bias=False,
          ),
          nn.Conv2d(
              C_in,
              C_out,
              (1, kernel_length),
              stride,
              padding,
              bias=False,
          ),
          nn.BatchNorm2d(C_out, affine=affine),
      )
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new FacConv object from inherited base classes: nn with example: obj = FacConv(C_in, C_out, kernel_length, stride, padding, affine)